Audio-visual communication over packet data networks has become more and more common. However, existing systems are lossy, i.e., data packets representing the signals to be transmitted over the packet data networks may be lost. Methods to address packet loss can be divided into two groups: receiver-based and sender-based methods. Sender-based methods, which introduce redundancy in the transmitted bit stream, are generally more powerful but require changes in both encoder and decoder, whereas receiver-based methods require changes in the decoder only. Many commonly used sender-based methods employ block channel codes as redundancy (e.g., forward error correction (FEC)) with a constant redundancy bit rate, thus protecting the primary encoding against packet loss. Other sender-based methods are based on multiple-description coding (MDC). A drawback of MDC-based strategies is that they cannot easily be added to existing systems and that the packet-loss robustness is integrated into the source coder. Other existing methods for sender-based recovery of lost packets are based on adding to a primary coding system vector quantizers (VQs) that must be trained off-line, which do not provide any flexibility in the coding of the signal to be transmitted since these vector quantizers require extra computation and/or predetermined lookup tables. These existing methods usually are dependent on the primary encoding through discrete statistical models, e.g., states of Markov models referring to VQ cells or on a particular packet-loss scenario. A problem is that these dependencies require different estimators or the training of several statistical models for different rates and different scenarios. Thus, there is a need for providing improved methods and devices that would overcome at least some of these problems.